通过轨迹聚类识别游戏中的集体行为机器人，提升反外挂效率。
A Framework for Mining Collectively-Behaving Bots in MMORPGs
- 基于无标签轨迹数据学习玩家行为嵌入表示。
- 利用DBSCAN聚类发现具有相似移动模式的异常账号群。
- 适合游戏安全团队用于批量识别自动化作弊行为。
在大型多人在线角色扮演游戏中，使用未经授权自动化程序执行重复性行为的异常玩家（即机器人）普遍存在。这些机器人通常为获取游戏内货币并兑换真实金钱，导致合法玩家的游戏体验受损，因它们垄断特定狩猎区域并抢夺珍贵物品。因此，检测异常玩家对游戏公司至关重要。我们观察到，由于自动化程序的特性，机器人常表现出高度一致的在游戏内轨迹行为。为此，我们提出BotTRep框架，包含轨迹表示学习与无监督聚类两阶段。该模型旨在学习游戏轨迹序列的嵌入表示，使具有上下文相似轨迹的玩家在向量空间中距离更近。随后，应用DBSCAN算法对嵌入进行聚类，并可视化对应的移动模式，最终帮助游戏管理员识别并封禁机器人账号。
原文摘要 · Abstract (English)
In MMORPGs (Massively Multiplayer Online Role-Playing Games), abnormal players (bots) using unauthorized automated programs to carry out pre-defined behaviors systematically and repeatedly are commonly observed. Bots usually engage in these activities to gain in-game money, which they eventually trade for real money outside the game. Such abusive activities negatively impact the in-game experiences of legitimate users since bots monopolize specific hunting areas and obtain valuable items. Thus, detecting abnormal players is a significant task for game companies. Motivated by the fact that bots tend to behave collectively with similar in-game trajectories due to the auto-programs, we developed BotTRep, a framework that comprises trajectory representation learning followed by clustering using a completely unlabeled in-game trajectory dataset. Our model aims to learn representations for in-game trajectory sequences so that players with contextually similar trajectories have closer embeddings. Then, by applying DBSCAN to these representations and visualizing the corresponding moving patterns, our framework ultimately assists game masters in identifying and banning bots.
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